Cargando…
Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning
The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged ≥18 years who fulfilled t...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9917876/ https://www.ncbi.nlm.nih.gov/pubmed/36769579 http://dx.doi.org/10.3390/jcm12030931 |
_version_ | 1784886473628057600 |
---|---|
author | Queiro, Rubén Seoane-Mato, Daniel Laiz, Ana Galindez Agirregoikoa, Eva Montilla, Carlos Park, Hye S. Tasende, Jose A. Pinto Baute, Juan J. Bethencourt Joven Ibáñez, Beatriz Toniolo, Elide Ramírez, Julio Montero, Nuria Pruenza García-Hinojosa, Cristina Serrano García, Ana |
author_facet | Queiro, Rubén Seoane-Mato, Daniel Laiz, Ana Galindez Agirregoikoa, Eva Montilla, Carlos Park, Hye S. Tasende, Jose A. Pinto Baute, Juan J. Bethencourt Joven Ibáñez, Beatriz Toniolo, Elide Ramírez, Julio Montero, Nuria Pruenza García-Hinojosa, Cristina Serrano García, Ana |
author_sort | Queiro, Rubén |
collection | PubMed |
description | The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged ≥18 years who fulfilled the CASPAR criteria and had less than 2 years since the onset of symptoms. The moderate-to-high activity of PsA was defined as DAPSA > 14. We trained a logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. The sample comprised 158 patients. At the first follow-up visit, 20.8% of the patients who attended the clinic had a moderate-to-severe disease. This percentage rose to 21.2% on the second visit. The variables predicting moderate-high activity were the PsAID score, tender joint count, level of physical activity, and sex. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (98%; 95% CI: 86.89–100.00). PsAID was the most important variable in the prediction algorithms, reinforcing the convenience of its inclusion in daily clinical practice. Strategies that focus on the needs of women with PsA should be considered. |
format | Online Article Text |
id | pubmed-9917876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99178762023-02-11 Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning Queiro, Rubén Seoane-Mato, Daniel Laiz, Ana Galindez Agirregoikoa, Eva Montilla, Carlos Park, Hye S. Tasende, Jose A. Pinto Baute, Juan J. Bethencourt Joven Ibáñez, Beatriz Toniolo, Elide Ramírez, Julio Montero, Nuria Pruenza García-Hinojosa, Cristina Serrano García, Ana J Clin Med Article The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged ≥18 years who fulfilled the CASPAR criteria and had less than 2 years since the onset of symptoms. The moderate-to-high activity of PsA was defined as DAPSA > 14. We trained a logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. The sample comprised 158 patients. At the first follow-up visit, 20.8% of the patients who attended the clinic had a moderate-to-severe disease. This percentage rose to 21.2% on the second visit. The variables predicting moderate-high activity were the PsAID score, tender joint count, level of physical activity, and sex. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (98%; 95% CI: 86.89–100.00). PsAID was the most important variable in the prediction algorithms, reinforcing the convenience of its inclusion in daily clinical practice. Strategies that focus on the needs of women with PsA should be considered. MDPI 2023-01-25 /pmc/articles/PMC9917876/ /pubmed/36769579 http://dx.doi.org/10.3390/jcm12030931 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Queiro, Rubén Seoane-Mato, Daniel Laiz, Ana Galindez Agirregoikoa, Eva Montilla, Carlos Park, Hye S. Tasende, Jose A. Pinto Baute, Juan J. Bethencourt Joven Ibáñez, Beatriz Toniolo, Elide Ramírez, Julio Montero, Nuria Pruenza García-Hinojosa, Cristina Serrano García, Ana Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning |
title | Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning |
title_full | Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning |
title_fullStr | Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning |
title_full_unstemmed | Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning |
title_short | Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning |
title_sort | moderate-high disease activity in patients with recent-onset psoriatic arthritis—multivariable prediction model based on machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9917876/ https://www.ncbi.nlm.nih.gov/pubmed/36769579 http://dx.doi.org/10.3390/jcm12030931 |
work_keys_str_mv | AT queiroruben moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT seoanematodaniel moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT laizana moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT galindezagirregoikoaeva moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT montillacarlos moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT parkhyes moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT tasendejoseapinto moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT bautejuanjbethencourt moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT jovenibanezbeatriz moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT tonioloelide moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT ramirezjulio moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT monteronuria moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT pruenzagarciahinojosacristina moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT serranogarciaana moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning AT moderatehighdiseaseactivityinpatientswithrecentonsetpsoriaticarthritismultivariablepredictionmodelbasedonmachinelearning |